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1. Algorithmic Trading: What Is It and How Does It Work? Algorithmic trading is a type of trading that uses complex algorithms to determine the best time to buy or sell a stock or other financial instrument. Algorithmic trading systems use mathematical models and computer programs to make decisions about when to buy and sell. These systems can scan markets for potential opportunities, monitor news and events, and execute trades according to predetermined rules. Algorithmic trading has become increasingly popular as technology and computing power have advanced, making it possible to execute trades faster and with greater precision. Algorithmic trading can be used to help traders achieve better returns and reduce risk by eliminating emotions from the decision-making process.
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Mar 13 AArch Webinar: From Models to Data – How GenAI Is Changing the Game for Data Scientists and Data Teams - DATAVERSITY
<p>DATE: March 13, 2025 TIME: 2:00 PM – 3:00 PM Eastern / 11:00 AM – 12:00 PM Pacific PRICE: Free to all attendees About the Webinar GenAI is changing how AI and data teams work together. Before, data scientists focused on models and algorithms, using tools like R. Now, the focus is on gathering all data in […]</p>
Enhancing Search Relevancy with Cohere Rerank 3.5 and Amazon OpenSearch Service | AWS Big Data Blog
In this blog post, we'll dive into the various scenarios for how Cohere Rerank 3.5 improves search results for best matching 25 (BM25), a keyword-based algorithm that performs lexical search, in addition to semantic search. We will also cover how businesses can significantly improve user experience, increase engagement, and ultimately drive better search outcomes by implementing a reranking pipeline.
Researchers from ETH Zurich and UC Berkeley Introduce MaxInfoRL: A New Reinforcement Learning Framework for Balancing Intrinsic and Extrinsic Exploration - MarkTechPost
Reinforcement Learning, despite its popularity in a variety of fields, faces some fundamental difficulties that refrain users from exploiting its full potential. To begin with, algorithms like PPO, which are widely used, suffer from the curse of sample inefficiency (the need for multiple episodes to learn basic actions). Moving on, Off-Policy methods like SAC and DrQ offer some immunity against the above problem. They are applicable in the real world while being compute-efficient, but they have drawbacks. Off-policy methods often require dense reward signals, which means their performance is undermined in rewards' sparsity or local optima. This suboptimality can be
Is chess-like AI really inapplicable in turn-based strategy games? - Game Development Stack Exchange
Obviously, trying to apply the min-max algorithm on the complete tree of moves works only for small games (I apologize to all chess enthusiasts, by "small" I do not mean "simplistic"). For typical ...
How Microsoft’s AI Ecosystem Outperforms Salesforce and AWS - Unite.AI
AI agents are autonomous systems designed to perform tasks that would typically require human involvement. By using advanced algorithms, these agents can handle a wide range of functions, from answering customer inquiries to predicting business trends. This automation not only streamlines repetitive processes but also allows human workers to focus on more strategic and creative […]